1. Neuroscience Needs Behavior: Correcting a Reductionist Bias
John W. Krakauer, Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver, David Poeppel 
Neuron 
Volume 93, Issue 3, Pages (February 2017)
DOI: /j.neuron
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2. Figure 1
The Multiple Potential Mappings between Neural Activity Patterns and Natural Behaviors
(A) Of all the possible activity patterns of a brain in a dish (big pale blue circle), only a subset of these (medium dark blue circle) will be relevant in behaving animals in their natural environment (big magenta circle).
(B) Designing behavioral tasks that are ecologically valid (small magenta circle) ensures discovery of neural circuits relevant to the naturalistic behavior (small blue circle). Tasks that elicit species-typical behaviors with species-typical signals are examples (see Box 1).
(C) In order to study animal behavior in the lab, the task studied (small white circle) might be so non-ecological it elicits neural responses (small blue circle) that are never used in natural behaviors.
(D) Multiple Realizability: different patterns of activity or circuit configurations (small blue circles) can lead to the same behavior (small magenta circle).
(E) The same neural activity pattern (small blue circle) can be used in two different behaviors (two magenta circles). The circle with dashed perimeter in (B)–(E) is the subset of all possible neural activity patterns that map onto natural behaviors (from A).
Neuron  , DOI: ( /j.neuron )
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3. Figure 2 Marr’s Three Levels of Analysis
(A) A bird attempts to fly (goal) by flapping its wings (algorithmic realization) whose aerodynamics depend on the features of its feathers (physical implementation). Feathers “have something to do” with flight and flapping, but what level of understanding do we achieve if we dissect the properties of the feathers alone? Bats fly but don’t have feathers, and birds can fly without flapping.
(B) The relationship between the three levels is not arbitrary; step 1 comes before step 2: the algorithmic level of understanding is essential to interpret its mechanistic implementation. Step 2: implementation level work feeds back to inform the algorithmic level.
(C) An epistemological bias toward manipulation-based view of understanding induced by technology (black filled arrow).
Neuron  , DOI: ( /j.neuron )
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4. Figure 3 The Interventionist Type of Understanding Is Not Sufficient
The current dominant notion of what constitutes understanding in neuroscience is based on an interventionist approach to causality.
(A) Intervening at the neural level (blue) as a way to explain the behavior (magenta) through necessity and sufficiency claims.
(B) The result that “X is necessary and sufficient for Y to occur” allows a causal claim. An additional explanatory sentence is often added but is merely the same causal result rearticulated with a “filler” verb.
(C) Common filler verbs.
Neuron  , DOI: ( /j.neuron )
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5. Figure 4 The Future History of Pluralistic Explanation
(A) That understanding of a phenomenon is multidimensional has long been appreciated. Aristotle posited four kinds of explanation: to explain “why” something changes, a polyhedric notion of causality is necessary; one that includes not only the material cause (what it is made out of), but also the other three “whys”: formal (what it is to be), efficient (what produces it), and final (what it is for). Tinbergen also devised four questions about behavior: to go beyond its proximate causation (mechanism) to also considering its evolution, development, and real-world function. Marr’s three levels are also shown.
(B) Three-dimensional space with axes of understanding-manipulation, behavior-neurons, and Marr’s levels. The red box is where we are and the blue is where we should be.
Neuron  , DOI: ( /j.neuron )
Copyright © 2017 Elsevier Inc. Terms and Conditions